{"paper_id":"0484f33f-e75d-4c3d-a4d2-f1dfc71baa38","body_text":"Ensemble Learning with Feature Optimization for Credit Risk Assessment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Ensemble Learning with Feature Optimization for Credit Risk Assessment Guanghui Zeng, Weixin Su, Chaoqun Hong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4665987/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Credit risk assessment stands as a cornerstone in financial decision-making, with significant implications for economic stability and growth. This paper highlights the transformative advantages of credit big data over traditional methods, particularly in enhancing the creditworthiness evaluation of small and medium-sized enterprises (SMEs). We delineate the distinctive features of the big data financial innovation model across six economic dimensions, showcasing its potential to reshape financial practices. To address the inefficiencies of traditional expert-driven approaches, we introduce an innovative 'Feature Selector-classifier Optimization Framework' that streamlines the credit risk prediction process. This framework not only refines the accuracy and efficiency of predictions but also integrates seamlessly with economic analysis, offering a robust tool for financial decision-makers. Our ensemble classifier delivers remarkable performance, exemplified by its high accuracy and AUC scores across multiple datasets, thereby validating the framework's efficacy in enhancing predictive power while ensuring operational efficiency. Credit risk assessment Machine learning Ensemble learning Feature Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4665987\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":331419340,\"identity\":\"708835df-c59c-4597-a2c9-79d774a9feb8\",\"order_by\":0,\"name\":\"Guanghui Zeng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Northeastern University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Guanghui\",\"middleName\":\"\",\"lastName\":\"Zeng\",\"suffix\":\"\"},{\"id\":331419341,\"identity\":\"617c2275-dd75-4cb3-afd2-d2d0dbf3402a\",\"order_by\":1,\"name\":\"Weixin Su\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xiamen University of Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Weixin\",\"middleName\":\"\",\"lastName\":\"Su\",\"suffix\":\"\"},{\"id\":331419342,\"identity\":\"87f08743-4a30-4e4f-82a5-ea9f8f09e52e\",\"order_by\":2,\"name\":\"Chaoqun Hong\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYFACHhBhA+WwEa8lDYiZSdNymAQt/O25Bx8X/DqfuHZG/gGGD2WHGfhnN+DXInHmXbLxzL7bxmY3khkYZ5w7zCBx5wB+LQYSOWbSvD235UBamHnbDgNFEojSco4HrOUv0Vp4fhyA2MJIjBaJM2+MjXkbko3Nzjw2ONhzLp1H4gYBLfztOYaPef7YJW47nvjwwY8yazn+GQS0MDAAFTC2QZgHGKDRRFgLwx8i1I2CUTAKRsHIBQC6AkBLe3kb/AAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Xiamen University of Technology\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Chaoqun\",\"middleName\":\"\",\"lastName\":\"Hong\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-07-01 07:06:42\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4665987/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4665987/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":71043396,\"identity\":\"fbdd7b64-086e-4f98-b421-7349d4d9277b\",\"added_by\":\"auto\",\"created_at\":\"2024-12-10 14:17:12\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":462399,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"EnsembleLearningwithFeatureOptimizationforCreditRiskAssessment.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4665987/v1_covered_2b8ecead-5c29-4231-9a29-acaa88fd9235.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Ensemble Learning with Feature Optimization for Credit Risk Assessment\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Credit risk assessment, Machine learning, Ensemble learning, Feature Optimization\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4665987/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4665987/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eCredit risk assessment stands as a cornerstone in financial decision-making, with significant implications for economic stability and growth. This paper highlights the transformative advantages of credit big data over traditional methods, particularly in enhancing the creditworthiness evaluation of small and medium-sized enterprises (SMEs). We delineate the distinctive features of the big data financial innovation model across six economic dimensions, showcasing its potential to reshape financial practices. To address the inefficiencies of traditional expert-driven approaches, we introduce an innovative 'Feature Selector-classifier Optimization Framework' that streamlines the credit risk prediction process. This framework not only refines the accuracy and efficiency of predictions but also integrates seamlessly with economic analysis, offering a robust tool for financial decision-makers. Our ensemble classifier delivers remarkable performance, exemplified by its high accuracy and AUC scores across multiple datasets, thereby validating the framework's efficacy in enhancing predictive power while ensuring operational efficiency. \\u003c/p\\u003e\",\"manuscriptTitle\":\"Ensemble Learning with Feature Optimization for Credit Risk Assessment\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-07-29 06:18:35\",\"doi\":\"10.21203/rs.3.rs-4665987/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"b4330e0b-8561-4288-adce-9d8845432b68\",\"owner\":[],\"postedDate\":\"July 29th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-12-10T14:08:55+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-07-29 06:18:35\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4665987\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4665987\",\"identity\":\"rs-4665987\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}